OpenRaiser/Intern-Atlas huggingface dataset:
Safety in Graph Machine Learning: Threats and Safeguards
Venue: arXiv cs.LG 2024
Year: 2024

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have been extensively utilized across diverse applications, including critical domains like finance, healthcare, and transportation. Despite their societal benefits, recent research highlights significant safety concerns associated with the widespread use of Graph ML models. Lacking safety-focused designs, these models can produce unreliable predictions, demonstrate poor generalizability, and compromise data confidentiality. In high-stakes scenarios such as financial fraud detection, these vulnerabilities could jeopardize both individuals and society at large. Therefore, it is imperative to prioritize the development of safety-oriented Graph ML models to mitigate these risks and enhance public confidence in their applications. In this survey paper, we explore three critical aspects vital for enhancing safety in Graph ML: reliability, generalizability, and confidentiality. We categorize and analyze threats to each aspect under three headings: model threats, data threats, and attack threats. This novel taxonomy guides our review of effective strategies to protect against these threats. Our systematic review lays a groundwork for future research aimed at developing practical, safety-centered Graph ML models. Furthermore, we highlight the significance of safe Graph ML practices and suggest promising avenues for further investigation in this crucial area.
From Generalist to Specialist: Improving Large Language Models for Medical Physics Using ARCoT
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have achieved remarkable progress, yet their application in specialized fields, such as medical physics, remains challenging due to the need for domain-specific knowledge. This study introduces ARCoT (Adaptable Retrieval-based Chain of Thought), a framework designed to enhance the domain-specific accuracy of LLMs without requiring fine-tuning or extensive retraining. ARCoT integrates a retrieval mechanism to access relevant domain-specific information and employs step-back and chain-of-thought prompting techniques to guide the LLM's reasoning process, ensuring more accurate and context-aware responses. Benchmarking on a medical physics multiple-choice exam, our model outperformed standard LLMs and reported average human performance, demonstrating improvements of up to 68% and achieving a high score of 90%. This method reduces hallucinations and increases domain-specific performance. The versatility and model-agnostic nature of ARCoT make it easily adaptable to various domains, showcasing its significant potential for enhancing the accuracy and reliability of LLMs in specialized fields.
Vectorization of Gradient Boosting of Decision Trees Prediction in the CatBoost Library for RISC-V Processors
Venue: arXiv cs.DC 2024
Year: 2024

The emergence and rapid development of the open RISC-V instruction set architecture opens up new horizons on the way to efficient devices, ranging from existing low-power IoT boards to future high-performance servers. The effective use of RISC-V CPUs requires software optimization for the target platform. In this paper, we focus on the RISC-V-specific optimization of the CatBoost library, one of the widely used implementations of gradient boosting for decision trees. The CatBoost library is deeply optimized for commodity CPUs and GPUs. However, vectorization is required to effectively utilize the resources of RISC-V CPUs with the RVV 0.7.1 vector extension, which cannot be done automatically with a C++ compiler yet. The paper reports on our experience in benchmarking CatBoost on the Lichee Pi 4a, RISC-V-based board, and shows how manual vectorization of computationally intensive loops with intrinsics can speed up the use of decision trees several times, depending on the specific workload. The developed codes are publicly available on GitHub.
Next-slot OFDM-CSI Prediction: Multi-head Self-attention or State Space Model?
Venue: arXiv cs.IT 2024
Year: 2024

The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL architecture candidates for specific one-sided and two-sided use cases such as channel state estimation (CSI) feedback, CSI prediction, beam management, and positioning. In this paper, we set focus on the CSI prediction task and study the performance and generalization of the two main DL layers that are being extensively benchmarked within the DL community, namely, multi-head self-attention (MSA) and state-space model (SSM). We train and evaluate MSA and SSM layers to predict the next slot for uplink and downlink communication scenarios over urban microcell (UMi) and urban macrocell (UMa) OFDM 5G channel models. Our numerical results demonstrate that SSMs exhibit better prediction and generalization capabilities than MSAs only for SISO cases. For MIMO scenarios, however, the MSA layer outperforms the SSM one. While both layers represent potential DL architectures for future DL-enabled 5G use cases, the overall investigation of this paper favors MSAs over SSMs.
What metrics of participation balance predict outcomes of collaborative learning with a robot?
Venue: arXiv cs.HC 2024
Year: 2024

One of the keys to the success of collaborative learning is balanced participation by all learners, but this does not always happen naturally. Pedagogical robots have the potential to facilitate balance. However, it remains unclear what participation balance robots should aim at; various metrics have been proposed, but it is still an open question whether we should balance human participation in human-human interactions (HHI) or human-robot interactions (HRI) and whether we should consider robots' participation in collaborative learning involving multiple humans and a robot. This paper examines collaborative learning between a pair of students and a teachable robot that acts as a peer tutee to answer the aforementioned question. Through an exploratory study, we hypothesize which balance metrics in the literature and which portions of dialogues (including vs. excluding robots' participation and human participation in HHI vs. HRI) will better predict learning as a group. We test the hypotheses with another study and replicate them with automatically obtained units of participation to simulate the information available to robots when they adaptively fix imbalances in real-time. Finally, we discuss recommendations on which metrics learning science researchers should choose when trying to understand how to facilitate collaboration.
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions
Venue: arXiv cs.MA 2024
Year: 2024

In recent years, Large Language Models (LLMs) have shown great abilities in various tasks, including question answering, arithmetic problem solving, and poem writing, among others. Although research on LLM-as-an-agent has shown that LLM can be applied to Reinforcement Learning (RL) and achieve decent results, the extension of LLM-based RL to Multi-Agent System (MAS) is not trivial, as many aspects, such as coordination and communication between agents, are not considered in the RL frameworks of a single agent. To inspire more research on LLM-based MARL, in this letter, we survey the existing LLM-based single-agent and multi-agent RL frameworks and provide potential research directions for future research. In particular, we focus on the cooperative tasks of multiple agents with a common goal and communication among them. We also consider human-in/on-the-loop scenarios enabled by the language component in the framework.
MotionGS : Compact Gaussian Splatting SLAM by Motion Filter
Venue: arXiv cs.CV 2024
Year: 2024

With their high-fidelity scene representation capability, the attention of SLAM field is deeply attracted by the Neural Radiation Field (NeRF) and 3D Gaussian Splatting (3DGS). Recently, there has been a surge in NeRF-based SLAM, while 3DGS-based SLAM is sparse. A novel 3DGS-based SLAM approach with a fusion of deep visual feature, dual keyframe selection and 3DGS is presented in this paper. Compared with the existing methods, the proposed tracking is achieved by feature extraction and motion filter on each frame. The joint optimization of poses and 3D Gaussians runs through the entire mapping process. Additionally, the coarse-to-fine pose estimation and compact Gaussian scene representation are implemented by dual keyframe selection and novel loss functions. Experimental results demonstrate that the proposed algorithm not only outperforms the existing methods in tracking and mapping, but also has less memory usage.
MultiPaxos Made Complete
Venue: arXiv cs.DC 2024
Year: 2024

MultiPaxos, while a fundamental Replicated State Machine algorithm, suffers from a dearth of comprehensive guidelines for achieving a complete and correct implementation. This deficiency has hindered MultiPaxos' practical utility and adoption and has resulted in flawed claims about its capabilities. Our paper aims to bridge the gap between MultiPaxos' complexity and practical implementation through a meticulous and detailed design process spanning more than a year. It carefully dissects each phase of MultiPaxos and offers detailed step-by-step pseudocode -- in addition to a complete open-source implementation -- for all components, including the leader election, the failure detector, and the commit phase. The implementation of our complete design also provides better performance stability, resource usage, and network partition tolerance than naive MultiPaxos versions. Our specification includes a lightweight log compaction approach that avoids taking repeated snapshots, significantly improving resource usage and performance stability. Our failure detector, integrated into the commit phase of the algorithm, uses variable and adaptive heartbeat intervals to settle on a better leader under partial connectivity and network partitions, improving liveness under such conditions.
Designing NLP Systems That Adapt to Diverse Worldviews
Venue: arXiv cs.CL 2024
Year: 2024

Natural Language Inference (NLI) is foundational for evaluating language understanding in AI. However, progress has plateaued, with models failing on ambiguous examples and exhibiting poor generalization. We argue that this stems from disregarding the subjective nature of meaning, which is intrinsically tied to an individual's \textit{weltanschauung} (which roughly translates to worldview). Existing NLP datasets often obscure this by aggregating labels or filtering out disagreement. We propose a perspectivist approach: building datasets that capture annotator demographics, values, and justifications for their labels. Such datasets would explicitly model diverse worldviews. Our initial experiments with a subset of the SBIC dataset demonstrate that even limited annotator metadata can improve model performance.
Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses
Venue: arXiv cs.LG 2024
Year: 2024

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data exploration inherent in RL by pre-training policies on vast amounts of offline data, enabling direct deployment or fine-tuning in real-world environments. However, this training paradigm can compromise policy robustness, leading to degraded performance in practical conditions due to observation perturbations or intentional attacks. While adversarial attacks and defenses have been extensively studied in deep learning, their application in offline RL is limited. This paper proposes a framework to enhance the robustness of offline RL models by leveraging advanced adversarial attacks and defenses. The framework attacks the actor and critic components by perturbing observations during training and using adversarial defenses as regularization to enhance the learned policy. Four attacks and two defenses are introduced and evaluated on the D4RL benchmark. The results show the vulnerability of both the actor and critic to attacks and the effectiveness of the defenses in improving policy robustness. This framework holds promise for enhancing the reliability of offline RL models in practical scenarios.
Transformer based neural networks for emotion recognition in conversations
Venue: arXiv cs.CL 2024
Year: 2024

This paper outlines the approach of the ISDS-NLP team in the SemEval 2024 Task 10: Emotion Discovery and Reasoning its Flip in Conversation (EDiReF). For Subtask 1 we obtained a weighted F1 score of 0.43 and placed 12 in the leaderboard. We investigate two distinct approaches: Masked Language Modeling (MLM) and Causal Language Modeling (CLM). For MLM, we employ pre-trained BERT-like models in a multilingual setting, fine-tuning them with a classifier to predict emotions. Experiments with varying input lengths, classifier architectures, and fine-tuning strategies demonstrate the effectiveness of this approach. Additionally, we utilize Mistral 7B Instruct V0.2, a state-of-the-art model, applying zero-shot and few-shot prompting techniques. Our findings indicate that while Mistral shows promise, MLMs currently outperform them in sentence-level emotion classification.
Bridge and Hint: Extending Pre-trained Language Models for Long-Range Code
Venue: arXiv cs.SE 2024
Year: 2024

In the field of code intelligence, effectively modeling long-range code poses a significant challenge. Existing pre-trained language models (PLMs) such as UniXcoder have achieved remarkable success, but they still face difficulties with long code inputs. This is mainly due to their limited capacity to maintain contextual continuity and memorize the key information over long-range code. To alleviate the difficulties, we propose EXPO, a framework for EXtending Pre-trained language models for lOng-range code. EXPO incorporates two innovative memory mechanisms we propose in this paper: Bridge Memory and Hint Memory. Bridge Memory uses a tagging mechanism to connect disparate snippets of long-range code, helping the model maintain contextual coherence. Hint Memory focuses on crucial code elements throughout the global context, such as package imports, by integrating a kNN attention layer to adaptively select the relevant code elements. This dual-memory approach bridges the gap between understanding local code snippets and maintaining global code coherence, thereby enhancing the model overall comprehension of long code sequences. We validate the effectiveness of EXPO on five popular pre-trained language models such as UniXcoder and two code intelligence tasks including API recommendation and vulnerability detection. Experimental results demonstrate that EXPO significantly improves the pre-training language models.
A User Interface Study on Sustainable City Trip Recommendations
Venue: arXiv cs.HC 2024
Year: 2024

The importance of promoting sustainable and environmentally responsible practices is becoming increasingly recognized in all domains, including tourism. The impact of tourism extends beyond its immediate stakeholders and affects passive participants such as the environment, local businesses, and residents. City trips, in particular, offer significant opportunities to encourage sustainable tourism practices by directing travelers towards destinations that minimize environmental impact while providing enriching experiences. Tourism Recommender Systems (TRS) can play a critical role in this. By integrating sustainability features in TRS, travelers can be guided towards destinations that meet their preferences and align with sustainability objectives. This paper investigates how different user interface design elements affect the promotion of sustainable city trip choices. We explore the impact of various features on user decisions, including sustainability labels for transportation modes and their emissions, popularity indicators for destinations, seasonality labels reflecting crowd levels for specific months, and an overall sustainability composite score. Through a user study involving mockups, participants evaluated the helpfulness of these features in guiding them toward more sustainable travel options. Our findings indicate that sustainability labels significantly influence users towards lower-carbon footprint options, while popularity and seasonality indicators guide users to less crowded and more seasonally appropriate destinations. This study emphasizes the importance of providing users with clear and informative sustainability information, which can help them make more sustainable travel choices. It lays the groundwork for future applications that can recommend sustainable destinations in real-time.
Cooperative Cognitive Dynamic System in UAV Swarms: Reconfigurable Mechanism and Framework
Venue: arXiv cs.DC 2024
Year: 2024

As the demands for immediate and effective responses increase in both civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge as effective solutions, in which multiple cooperative UAVs can work together to achieve specific goals. However, how to manage such complex systems to ensure real-time adaptability lack sufficient researches. Hence, in this paper, we propose the cooperative cognitive dynamic system (CCDS), to optimize the management for UAV swarms. CCDS leverages a hierarchical and cooperative control structure that enables real-time data processing and decision. Accordingly, CCDS optimizes the UAV swarm management via dynamic reconfigurability and adaptive intelligent optimization. In addition, CCDS can be integrated with the biomimetic mechanism to efficiently allocate tasks for UAV swarms. Further, the distributed coordination of CCDS ensures reliable and resilient control, thus enhancing the adaptability and robustness. Finally, the potential challenges and future directions are analyzed, to provide insights into managing UAV swarms in dynamic heterogeneous networking.
Cloud Security and Security Challenges Revisited
Venue: arXiv cs.CR 2024
Year: 2024

In recent years, Cloud Computing has transformed local businesses and created new business models on the Internet- and Cloud services are still flourishing. But after the emphatic hype in the early years, a more realistic perception of Cloud services has emerged. One reason for this surely is that today, Cloud Computing is considered as an established and well-accepted technology and no longer as a technical novelty. But the second reason for this assessment might also be numerous security issues that Cloud Computing in general or specific Cloud services have experienced since then. In this paper, we revisit attacks on Cloud services and Cloud-related attack vectors that have been published in recent years. We then consider successful or proposed solutions to cope with these challenges. Based on these findings, we apply a security metric in order to rank all these Cloud-related security challenges concerning their severity. This should assist security professionals to prioritize their efforts toward addressing these issues.
Characterizing the Complexity of Social Robot Navigation Scenarios
Venue: arXiv cs.RO 2024
Year: 2024

Social robot navigation algorithms are often demonstrated in overly simplified scenarios, prohibiting the extraction of practical insights about their relevance to real-world domains. Our key insight is that an understanding of the inherent complexity of a social robot navigation scenario could help characterize the limitations of existing navigation algorithms and provide actionable directions for improvement. Through an exploration of recent literature, we identify a series of factors contributing to the complexity of a scenario, disambiguating between contextual and robot-related ones. We then conduct a simulation study investigating how manipulations of contextual factors impact the performance of a variety of navigation algorithms. We find that dense and narrow environments correlate most strongly with performance drops, while the heterogeneity of agent policies and directionality of interactions have a less pronounced effect. Our findings motivate a shift towards developing and testing algorithms under higher-complexity settings.
Assessing Group Fairness with Social Welfare Optimization
Venue: arXiv cs.AI 2024
Year: 2024

Statistical parity metrics have been widely studied and endorsed in the AI community as a means of achieving fairness, but they suffer from at least two weaknesses. They disregard the actual welfare consequences of decisions and may therefore fail to achieve the kind of fairness that is desired for disadvantaged groups. In addition, they are often incompatible with each other, and there is no convincing justification for selecting one rather than another. This paper explores whether a broader conception of social justice, based on optimizing a social welfare function (SWF), can be useful for assessing various definitions of parity. We focus on the well-known alpha fairness SWF, which has been defended by axiomatic and bargaining arguments over a period of 70 years. We analyze the optimal solution and show that it can justify demographic parity or equalized odds under certain conditions, but frequently requires a departure from these types of parity. In addition, we find that predictive rate parity is of limited usefulness. These results suggest that optimization theory can shed light on the intensely discussed question of how to achieve group fairness in AI.
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
Venue: arXiv cs.CL 2024
Year: 2024

Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard to balance accuracy and efficiency. A longer (shorter) soft prompt generally leads to a better(worse) accuracy but at the cost of more (less) training time. (ii)The performance may not be consistent when adapting to different downstream tasks. We attribute it to the same embedding space but responsible for different requirements of downstream tasks. To address these issues, we propose an Efficient Prompt Tuning method (EPT) by multi-space projection and prompt fusion. Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, significantly reducing the training time. Accuracy is also enhanced by leveraging low-rank matrices and the short prompt as additional knowledge sources to enrich the semantics of the original short prompt. In addition, we project the soft prompt into multiple subspaces to improve the performance consistency, and then adaptively learn the combination weights of different spaces through a gating network. Experiments on 13 natural language processing downstream tasks show that our method significantly and consistently outperforms 11 comparison methods with the relative percentage of improvements up to 12.9%, and training time decreased by 14%.
Effective In-Context Example Selection through Data Compression
Venue: arXiv cs.CL 2024
Year: 2024

In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingredient in this approach, lacks systematic and in-depth research. In this paper, we propose a data compression approach to the selection of in-context examples. We introduce a two-stage method that can effectively choose relevant examples and retain sufficient information about the training dataset within the in-context examples. Our method shows a significant improvement of an average of 5.90% across five different real-world datasets using four language models.